# Automated Query Reformulation for Efficient Search based on Query Logs From Stack Overflow

arXiv: https://arxiv.org/abs/2102.00826 · HTML: https://mlsanity.com/paper/2102.00826/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 82 references that are in the map.
- First posted on arXiv 1 Feb 2021.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [Generating Wikipedia by Summarizing Long Sequences](https://mlsanity.com/paper/1801.10198/) (2018)
- [Learning Deep Transformer Models for Machine Translation](https://mlsanity.com/paper/1906.01787/) (2019)
- [Recent Trends in Deep Learning Based Natural Language Processing](https://mlsanity.com/paper/1708.02709/) (2017)
- [Constituency Parsing with a Self-Attentive Encoder](https://mlsanity.com/paper/1805.01052/) (2018)
- [Tensor2Tensor for Neural Machine Translation](https://mlsanity.com/paper/1803.07416/) (2018)
- [A Hierarchical Recurrent Encoder-Decoder For Generative Context-Aware Query Suggestion](https://mlsanity.com/paper/1507.02221/) (2015)
- [Unblind Your Apps: Predicting Natural-Language Labels for Mobile GUI Components by Deep Learning](https://mlsanity.com/paper/2003.00380/) (2020)
- [GLEU Without Tuning](https://mlsanity.com/paper/1605.02592/) (2016)
- [Query Expansion Based on Crowd Knowledge for Code Search](https://mlsanity.com/paper/1703.01443/) (2017)
- [Context Attentive Document Ranking and Query Suggestion](https://mlsanity.com/paper/1906.02329/) (2019)
- [A Joint Model for Word Embedding and Word Morphology](https://mlsanity.com/paper/1606.02601/) (2016)
- [Attention-based Hierarchical Neural Query Suggestion](https://mlsanity.com/paper/1805.02816/) (2018)
- [Improved Query Reformulation for Concept Location using CodeRank and Document Structures](https://mlsanity.com/paper/1807.04488/) (2018)
- [Effective Reformulation of Query for Code Search using Crowdsourced Knowledge and Extra-Large Data Analytics](https://mlsanity.com/paper/1807.08798/) (2018)
- [QUICKAR: Automatic Query Reformulation for Concept Location using Crowdsourced Knowledge](https://mlsanity.com/paper/1807.02964/) (2018)
- [A Term-Based Methodology for Query Reformulation Understanding](https://mlsanity.com/paper/1601.04615/) (2016)

## Built on by (most cited first, 4 of 4)

- [BashExplainer: Retrieval-Augmented Bash Code Comment Generation based on Fine-tuned CodeBERT](https://mlsanity.com/paper/2206.13325/) (2022)
- [A Systematic Review of Automated Query Reformulations in Source Code Search](https://mlsanity.com/paper/2108.09646/) (2021)
- [SPICED: News Similarity Detection Dataset with Multiple Topics and Complexity Levels](https://mlsanity.com/paper/2309.13080/) (2023)
- [Improving Document Retrieval Coherence for Semantically Equivalent Queries](https://mlsanity.com/paper/2508.07975/) (2025)

## Abstract (arXiv)

As a popular Q&A site for programming, Stack Overflow is a treasure for developers. However, the amount of questions and answers on Stack Overflow make it difficult for developers to efficiently locate the information they are looking for. There are two gaps leading to poor search results: the gap between the user's intention and the textual query, and the semantic gap between the query and the post content. Therefore, developers have to constantly reformulate their queries by correcting misspelled words, adding limitations to certain programming languages or platforms, etc. As query reformulation is tedious for developers, especially for novices, we propose an automated software-specific query reformulation approach based on deep learning. With query logs provided by Stack Overflow, we construct a large-scale query reformulation corpus, including the original queries and corresponding reformulated ones. Our approach trains a Transformer model that can automatically generate candidate reformulated queries when given the user's original query. The evaluation results show that our approach outperforms five state-of-the-art baselines, and achieves a 5.6% to 33.5% boost in terms of mathitExactMatch and a 4.8% to 14.4% boost in terms of mathitGLEU.